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With the rapid development of smart grids, the strategic behavior evolution in user-side electricity market transactions has become increasingly complex. To explore the dynamic evolution mechanisms in this area, this paper systematically reviews the application of evolutionary game theory in user-side electricity markets, focusing on its unique advantages in modeling multi-agent interactions and dynamic strategy optimization. While evolutionary game theory excels in explaining the formation of long-term stable strategies, it faces limitations when dealing with real-time dynamic changes and high-dimensional state spaces. Thus, this paper further investigates the integration of deep reinforcement learning, particularly the deep Q-learning network (DQN), with evolutionary game theory, aiming to enhance its adaptability in electricity market applications. The introduction of the DQN enables market participants to perform adaptive strategy optimization in rapidly changing environments, thereby more effectively responding to supply–demand fluctuations in electricity markets. Through simulations based on a multi-agent model, this study reveals the dynamic characteristics of strategy evolution under different market conditions, highlighting the changing interaction patterns among participants in complex market environments. In summary, this comprehensive review not only demonstrates the broad applicability of evolutionary game theory in user-side electricity markets but also extends its potential in real-time decision making through the integration of modern algorithms, providing new theoretical foundations and practical insights for future market optimization and policy formulation.
With the rapid development of smart grids, the strategic behavior evolution in user-side electricity market transactions has become increasingly complex. To explore the dynamic evolution mechanisms in this area, this paper systematically reviews the application of evolutionary game theory in user-side electricity markets, focusing on its unique advantages in modeling multi-agent interactions and dynamic strategy optimization. While evolutionary game theory excels in explaining the formation of long-term stable strategies, it faces limitations when dealing with real-time dynamic changes and high-dimensional state spaces. Thus, this paper further investigates the integration of deep reinforcement learning, particularly the deep Q-learning network (DQN), with evolutionary game theory, aiming to enhance its adaptability in electricity market applications. The introduction of the DQN enables market participants to perform adaptive strategy optimization in rapidly changing environments, thereby more effectively responding to supply–demand fluctuations in electricity markets. Through simulations based on a multi-agent model, this study reveals the dynamic characteristics of strategy evolution under different market conditions, highlighting the changing interaction patterns among participants in complex market environments. In summary, this comprehensive review not only demonstrates the broad applicability of evolutionary game theory in user-side electricity markets but also extends its potential in real-time decision making through the integration of modern algorithms, providing new theoretical foundations and practical insights for future market optimization and policy formulation.
Vaccination is the key to interrupting the transmission of viruses, reducing public health losses, and improving the efficiency of public health emergency management. The implementation of vaccination requires communication between the government and the public, and the participation of multiple subjects. Strengthening the coordination of multiple subjects in the process of vaccination can improve the vaccination rate and broaden its scope. Therefore, from the perspective of inter-organizational interaction, a public health emergency vaccination game model based on health management departments, vaccinologists, and the public was constructed in this study. With the objective of improving the effectiveness of vaccination, the influential factors in a public health emergency vaccination game system and game subjects’ strategy selection were explored using a numerical simulation analysis. The research results showed that the range of vaccination, the diversification of vaccination information release, the level of emergency coordination between health management departments and vaccinologists, and the public’s awareness of emergency protection can all effectively promote vaccination. Among them, the effects of vaccination range (δ) and the diversification of vaccination information release (φ) on game subjects’ strategy selection fluctuated, but did not affect the overall trend. Both the level of emergency collaboration (θ) and public safety awareness (ε) can enhance the initiative of game subjects to participate in vaccination. When the stable strategy combination formed by the game system are positive promotion strategy, active guidance strategy and active vaccination strategy, the convergence rate of health management departments and vaccinologists to form a stable strategy is greater than that of the public. Further, the implications of promoting the effective implementation of vaccination are put forward via improving the vaccination strategy, strengthening vaccination collaboration, mobilizing the enthusiasm of vaccinologists, and enhancing the initiative of the public.
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